Using computer vision to identify mosquito larvae species
Automated identification of larval mosquito species with computer vision
This study is working on a smart system that uses computer technology to quickly identify different types of mosquito larvae, helping to improve mosquito control efforts and keep you safe from bites.
Quick facts
| Grant type | Sbir 1 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Vectech, LLC NIH-funded |
| Lab location | 1 site (Baltimore, UNITED STATES) |
| Project ID | NIH-11075797 on NIH RePORTER |
What this research studies
This research focuses on developing an automated system that uses computer vision technology to identify different species of mosquito larvae. By monitoring breeding sites and understanding the composition and abundance of these larvae, the project aims to enhance mosquito control efforts. The approach involves collecting larval samples and utilizing artificial intelligence algorithms to classify them accurately, which can lead to timely interventions before adult mosquitoes emerge. This method seeks to overcome the limitations of traditional larval surveillance, which is often resource-intensive and prone to delays.
Who could benefit from this research
Good fit: Ideal candidates for this research are individuals living in areas prone to mosquito-borne diseases, particularly those under 21 years old.
Not a fit: Patients who do not reside in mosquito-prone areas or who are not at risk of mosquito-borne diseases may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could significantly improve mosquito control strategies, reducing the incidence of mosquito-borne diseases.
How similar studies have performed: While there have been efforts to use crowdsourcing and unmanned aircraft systems for mosquito surveillance, this automated identification approach is relatively novel and untested.
Where this research is happening
Baltimore, UNITED STATES
- Vectech, LLC — Baltimore, United States (Active)
Researchers
- Principal investigator: Faiman, Roy — Vectech, LLC
- Study coordinator: Faiman, Roy
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.